Triple
T14838055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tancred |
E348883
|
entity |
| Predicate | prequel |
P1961
|
FINISHED |
| Object | Sybil |
E403980
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Sybil | Statement: [Tancred, prequel, Sybil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sybil Context triple: [Tancred, prequel, Sybil]
-
A.
Sybil
Sybil is a character from the fantasy film "The Magic Sword," known for her role in the story’s magical and adventurous narrative.
-
B.
Sybil
chosen
Sybil was an illegitimate daughter of King Henry I of England, known primarily through her royal lineage and connections within the Anglo-Norman nobility.
-
C.
Sybil
Sybil is an American R&B and pop singer best known for her late-1980s and early-1990s hits, including popular covers of classic soul songs.
-
D.
Sybil
Sybil is a 1976 television film about a woman with dissociative identity disorder, best known for Sally Field’s acclaimed, Emmy-winning performance in the title role.
-
E.
Sybil
Sybil is a feminine given name of Greek origin, historically associated with prophetesses and later borne by various notable women in arts and literature.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded28d0ddc8190a34e3e2d469ab762 |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b4b31a48190a3b60f02b581fbd2 |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:52 a.m.